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Wily Weasels: The Math Behind Mustelid Identification

The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link

Weasels are the most commonly misidentified animal in the entire Snapshot Wisconsin dataset. What might make them so tricky to identify, and just how do they differ from the other members of their family for whom they are often mistaken, like marten or mink?

As far as their phylogenic standing, weasels belong to the superfamily Musteloidea. Contained within Musteloidea are the families Mephitidae, which includes skunks; Mustelidae, including weasels, otters, ferrets and kin; and Procyonidae, with raccoons and their neotropical brethren. In examining the dataset of Snapshot Wisconsin photos that have received an expert classification, researchers have determined that weasels and mink are the two most difficult species for volunteers to classify.

Phylogenetic_Tree

The two avenues for classification available to volunteers through Snapshot Wisconsin are MySnapshot and Zooniverse. MySnapshot is the outlet available to those who monitor trail cameras, where they can classify the animals in the photos captured on their own camera. Zooniverse is a public forum where volunteers classify photos that are served up at random from Snapshot Wisconsin cameras across the state. Photos are captured in sets of three, called “triggers”, and volunteers classify the entire set at once.

recall_precision4When evaluating accuracy in classification, researchers focus on two variables: recall and precision. Both variables provide measures of accuracy for a group of volunteer classifiers, either from MySnapshot or Zooniverse, compared to expert classification. Recall addresses the question: out of all the weasel triggers in our dataset, how many did volunteers classify as weasels? Whereas precision addresses the question: how many triggers classified as weasels by volunteers were actually weasels?

Between both MySnapshot and Zooniverse, volunteers generally demonstrate high recall and precision when classifying animals that belong to the whole superfamily Musteloidea. When it comes to classifying individual species, we can see that animals like skunks, otters and raccoons are easier to classify correctly on account of their distinctive traits, but weasels are quite similar in physical appearance to the species with whom they share a family, namely mink. This makes weasels particularly easy to misidentify.

Triggers containing weasels and mink are most often missed completely on Zooniverse, with a recall value for these species of 41%. Out of an expertly classified sample size of 15 weasels, only 10 of the 15 were identified correctly by volunteers and 4 additional triggers classified as weasels on Zooniverse were not weasels. This puts recall and precision for weasel classification at a measly 67% and 71%, respectively.

For mink, Zooniverse and MySnapshot share low recall, with approximately 65% of mink photos missed completely on Zooniverse, and 39% missed on MySnapshot. However, the triggers that were classified were largely classified correctly, with perfect precision on Zooniverse and 87% precision on MySnapshot.

So how can we successfully identify a weasel versus some other mustelid, and vice versa? There are three types of weasels in Wisconsin. The long-tailed weasel is the largest of the three. They are typically 13-18 inches in length with a 4-6 inch black-tipped tail. Their coats are brown and their bellies and throats are cream-colored, though they transition completely to white in the winter. The short-tailed weasel is Wisconsin’s most common weasel. Smaller than the long-tailed weasel, the two share their coloring, which makes them more difficult to differentiate. The only discernible difference is the tail length. The third type of weasel is the least weasel, aptly named as it is the smallest of the three at roughly 6 inches. Though this weasel has coloring similar to the others, the least weasel has a short tail without the black tip.

Weasels look rather similar to mink, though mink are dark-colored and larger than weasels with long tails and glossy coats. They weigh between 1.5-2 lbs. Another mustelid closely resembling the weasel is the American pine marten, an endangered furbearer with a penchant for climbing. They have large rounded ears and a bushy tail, and their fur varies in shades of brown from almost yellow to almost black. Snapshot Wisconsin has only one confirmed photo of a marten, as the species is incredibly rare in Wisconsin.

Badgers seem like a no-brainer, with their characteristic striped heads and wide bodies. They are significantly larger than weasels and have long claws well-suited to digging. Despite their distinctive appearance, badgers are subject to misidentification as well. 22% of the triggers classified as badgers on MySnapshot were not badgers. The same goes for the fisher, another sizeable mustelid weighing in at an average of 15 pounds, with dark brown fur and a bushy tail.

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Classification is a tricky business, especially when it comes down to mustelids. Snapshot Wisconsin relies on thousands of volunteers to classify the nearly 34 million photos in our dataset, which they generally do with tremendous success. Though the weasel is a trickster, their phylogenic camouflage can be discerned with a trained eye – the same can be said for their Mustelidae cousins. Each accurately classified photo, mustelid or no, brings Snapshot Wisconsin closer to a complete representation of Wisconsin wildlife, and better informs our management of these species.

Snowy Spotting: Wisconsin’s White Deer

The following piece was written by OAS Communications Assistant Claire VanValkenburg for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link

Photo by Snapshot Wisconsin volunteer John Gorman

Although white-tailed deer are common in Wisconsin woods, we never seem to grow tired of pointing them out along the highway, watching them traverse our backyards and observing them on hikes. But as temperatures drop and snow falls, certain members of our herd will become harder to spot.

Al, a Vilas County trail camera host, recounts seeing one of these alabaster animals in 1994: “As we were sitting on the deck, a white deer appeared in the bright sunshine on the far side of the lake. It appeared to not only be white but glowing with a light from within, surrounded by the light green spring vegetation,” Al says.

Although seeing one can be striking, white deer are just like normal deer except for their fair coats. DNR researchers sampled the entire Snapshot Wisconsin dataset to piece together this map of where white deer were flagged using comments on MySnapshot and tagged photos on Zooniverse. They found that sightings occurred in 12 counties across the state, but were most frequent in central and northern Wisconsin.

WhiteDeerSitesMap

A white deer’s coloring is caused by a lack of melanin in the deer’s skin, but they aren’t necessarily always albino. True albinism is extremely rare, and an albino deer would have pink eyes, ears, hooves, all-white fur and poor eyesight. It’s more likely that Wisconsin’s white deer population is mostly leucistic, which is caused by a recessive genetic trait found in about 1% of all white-tails.

Leucistic deer (commonly referred to as “piebald deer”) can have a variety of white, brown or black markings. Some leucistic deer are akin to pinto horses, while others may have a completely bleached coat with black hooves and noses. The trick to identifying a leucistic deer from an albino is in the eyes. Deer with pink eyes are most likely true albinos, whereas piebalds have gray, blue or black eyes because albinism affects eyes whereas leucism does not.

While differentiating the two may be tricky, the law is clear. According to page 21 of the 2019 Wisconsin DNR Deer Hunting Regulations, it is illegal to “possess albino or all-white deer which are entirely white except for the hooves, tarsal glands, head and parts of the head unless special written authorization is obtained from the department.” Currently, this is true in all areas of Wisconsin.

Photo by Snapshot Wisconsin volunteer John Gorman

Nature photographer Jeff Richter came eye-to-eye with a white deer nearly two decades ago, and he’s been dedicated to capturing their beauty on camera ever since. Richter is the photographer of the book “White Deer: Ghosts of the Forests” by John Bates and told In Wisconsin Reporter Jo Garrett that despite the photographs, some people still believe they’re made up.

“We’ve actually had a couple of stores where clerks overheard people that’ve picked up the book and were looking at it and said, ‘Boy this is really neat, if only they were real,’” Richter said.

They’re real, alright. The one Al spotted all those years ago left a palpable impression on Al and his wife who, at the time, were looking to buy a house. They spotted the white deer across the lake when they were considering the purchase of what is now their current home.

“It actually may have had a small influence on the purchase of our home!” Al says.

Whether they’re common in your neck of the woods or not, white deer are striking animals. The stark contrast of their coats against Wisconsin woodlands makes them a pearly find among the herd. Keep your eyes peeled and your camera ready now, before they blend into a blanket of snow, perfectly hidden throughout the winter months.

Moving Through the Seasons: Annual Activity Patterns in Deer

The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link

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Wisconsin is renowned for being home to a well-established population of white-tailed deer. They are an undeniably important part of Wisconsin’s forests and farmland and are the animal that appears most frequently on Snapshot Wisconsin trail cameras. Since its inception in 2016 the project has accrued a massive supply of deer photos. This vital cache of information offers researchers the opportunity to make population-level observations about things like movement and activity patterns, and how these change with the seasons.

Deer_Age_Sex_Class_BreakdownOut of a sample of 1.4 million Snapshot Wisconsin trail camera photos of deer, antlerless deer take the lead at 63%. The remaining 37% comprises antlered (13%), adult unknown (15%) and fawns (8%).

In their ongoing analyses of these photos, scientists at the Wisconsin DNR have noted that deer show strong crepuscular patterns near both the summer and winter solstices. The word crepuscular refers to the interim between night and day, or both dawn and dusk. Deer are more active closer to sunrise and sunset than they are at any other time of day across any season. In winter, there is an observable preference toward sunset – most likely because afternoon is the warmest time of the day, and therefore the best for foraging. The opposite goes for the longer days in summer, when deer seem to prefer sunrise, as it is cooler and foraging at that time is less energetically expensive.

Crepuscular_seasonsDuring the summer, antlered deer are the most likely to stick to this crepuscular pattern. On the other hand, antlerless deer and fawns are a little more unpredictable. Fawns are generally more active throughout the day, as are antlerless deer, though to a lesser extent. Antlerless deer are also more active through the night. Assuming that most antlerless deer are does, their deviation from the crepuscular pattern can be attributed to their need to move to and from spots where they drop their fawns in the time following birth. Despite their penchant for daytime activity, after the first 12 weeks fawns begin to mirror the activity of their mothers, gradually falling into the recognizable crepuscular pattern.

 

Crepuscular_summerAs for winter, both antlered and antlerless deer are seen to be most active at sunset. At this point, fawns are indistinguishable from does and are therefore not differentiated from the rest of the population in the analysis. During the winter, antlered deer are more active during the night and less active during the day than antlerless. Predictably, the annual rut drives a significant uptick in activity for male deer in late October, a pattern familiar to Wisconsin drivers who may be liable to encounter deer more frequently around the same time.

Crepuscular_Winter1Snapshot Wisconsin’s growing cache of deer photos sheds light on deer activity as it varies through the seasons and even day-to-day. Recognizing these patterns bolsters our knowledge of how deer interact with and move through the landscape. These photos are also used to investigate population dynamics and determine fawn-to-doe ratios to better track the growth of the herd. Such a plentiful supply of information on an important species like deer is of great value to our researchers, whose analyses are a critical component of wildlife management decision-making at the Wisconsin DNR.

Whooping Crane Sighting

The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link

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Whooping crane 1-17 is, according to his personal biography, a natural-born leader. He is confident, vigilant, quick to take a jab at a potential threat and allegedly able to spot a worm at 50 yards.

This two-year-old crane, born and raised from captive breeding stock at the Patuxent Wildlife Research Center in Laurel, Maryland, is one of a rare species that has been newly restored to North America after overexploitation in the mid-20th century nearly drove them to extinction.

Whooping crane 1-17 appeared this spring on a Snapshot Wisconsin camera in Jackson County, much to the excitement of the Snapshot Wisconsin crew and the researchers stewarding 1-17’s journey across the landscape. View an interactive story map outlining 1-17’s journey here

“The conservation story behind [whooping cranes] is a marvelous story, involving a lot of effort and a lot of money,” said Davin Lopez, conservation biologist with the National Heritage Conservation Bureau in the Wisconsin Department of Natural Resources. “Although they’re recovering, they’re an incredibly rare species – I mean, people come from far and wide to see them – they’re a big, five-foot-tall, charismatic, pure white bird, so they’re pretty striking out there, very visible on the landscape. People find them very beautiful.”

Efforts toward the reintroduction of migratory whooping cranes to eastern North America began in 1999 with the formation of the Whooping Crane Eastern Partnership (WCEP). WCEP was founded as a collaborative project between the U.S. Fish and Wildlife Service, the International Crane Foundation, Operation Migration, the Wisconsin Department of Natural Resources, the United States Geological Survey, Patuxent Wildlife Research Center in Maryland, the USGS National Wildlife Health Center, the International Whooping Crane Recovery Team, the National Fish and Wildlife Foundation and the Natural Resources Foundation of Wisconsin.

The whooping crane is critically endangered in North America and has only one major migratory population, the Aransas-Wood Buffalo population (AWBP). This flock breeds in Canada, winters in Texas and comprises 505 birds as of December 2018.

Per the stipulations of the International Whooping Crane Recovery Plan, which outlined the need to establish one or more migratory crane populations in addition to the AWBP, WCEP has overseen the successful establishment of an eastern migratory population (EMP) of whooping cranes. In 2001, 7 individual cranes were guided in their migration from Wisconsin to Florida by aircraft, and 6 were guided back in the spring. As of July 2019, the estimated population size of the EMP is 87 individuals.

One significant barrier to the growth of wild whooping crane populations is the high mortality rate amongst wild chicks. Since 2002, WCEP has supplemented the wild crane population with chicks raised in captivity. These chicks were originally raised through costume-rearing, wherein the chick is raised by a human in costume. In recent years, researchers have transitioned to parent-rearing, where birds are raised in captivity by adult cranes, with minimal human intervention. 1-17 himself was a supplemental, costume-reared chick.

“Depending on the year, we get a certain number of chicks to release to supplement our wild population, and the bird in question, 1-17, is one of those birds. We also rely on natural reproduction, though historically we haven’t had a lot of it. That’s been one of our major struggles,” said Lopez. “Ultimately, we want to get to the point where we have a self-sustaining population out there that is above 100 birds at least, where we wouldn’t have to supplement any more birds.”

Crane 1-17 began his journey in the fall of 2017 in White River Marsh in Wisconsin with his sisters 2-17 and 8-17. When the birds failed to migrate on their own in a timely manner, they were relocated to Goose Pond in Indiana, and from there the trio flew to Talladega County in Alabama, where they spent the winter.

Come spring, the three cranes aimed north, but it swiftly became clear to researchers that they did not know the way back to Wisconsin. They spent some time in Illinois, and then 1-17 and 2-17 split from their sister and moved on to summer in Iowa.

At the end of November 2018, 1-17 and 2-17 took off from a stint in Northern Illinois and headed for the Wheeler National Wildlife Refuge in Alabama. Though they were met with a snowstorm en route, the pair persevered and were briefly reunited with 8-17 before she migrated to Tennessee in December.

In spring of 2019, 1-17 and 2-17 were observed wandering north and south through Indiana and Illinois, and eventually they found their way back to Wisconsin. The pair went their separate ways in April of 2019, and 1-17 was soon after captured on a Snapshot Wisconsin camera in Jackson County.

There is something of a learning curve when it comes to migratory behavior, and, as demonstrated by crane 1-17, there is sometimes a significant amount of wandering before whooping cranes grow to a reproductive age and settle.

The privilege of seeing one of these magnificent birds may be reserved for the select few who happen upon them out of sheer luck, but the population has been stable for several years and researchers look to the future of this species with optimism. Rare species like the whooping crane also become more visible as the state’s capacity for monitoring wildlife expands, and Snapshot Wisconsin spearheads this mission with a growing network of trail cameras posted throughout the landscape. As the project progresses, it will become easier to track the species that typically go undetected in wildlife surveys, better informing conservation efforts as well as broadening the public’s experience of Wisconsin wildlife.

“If you want to see a crane, we hope that you’re able to go out and find one,” said Lopez. “They may be rare in Jackson County, but we hope they’re a permanent fixture on the landscape in Wisconsin during the summer.”

May Science Update: Maintaining Quality in “Big Data”

Snapshot Wisconsin relies on different sources to help classify our growing dataset of more than 27 million photos, including our trail camera hosts, Zooniverse volunteers and experts at Wisconsin DNR. With all these different sources, we need ways to assess the quality and accuracy of the data before it’s put into the hands of decision makers.

A recent publication in Ecological Applications by Clare et. al (2019) looked at the issue of maintaining quality in “big data” by examining Snapshot Wisconsin images. The information from the study was used to develop a model that will help us predict which photos are most likely to contain classification errors. Because Snapshot-specific data were used in this study, we can now use these findings to decide which data to accept as final and which images would be best to go through expert review.

Perhaps most importantly, this framework allows us to be transparent with data users by providing specific metrics on the accuracy of our dataset. These confidence measures can be considered when using the data as input for models, when choosing research questions, and when interpreting the data for use in management decision making.

False-positive, false-negative

The study examined nearly 20,000 images classified on the crowdsourcing platform, Zooniverse. Classifications for each specie were analyzed to identify the false-negative error probability (the likelihood that a species is indicated as not present when it is) and the false-positive error probability (the likelihood that a species is indicated as present when it is not).

false_negative_graph

Figure 2 from Clare et al. 2019 – false-negative and false-positive probabilities by species, estimated from expert classification of the dataset. Whiskers represent 95% confidence intervals and the gray shading in the right panel represents the approximate probability required to produce a dataset with less than 5% error.

The authors found that classifications were 93% correct overall, but the rate of accuracy varied widely by species. This has major implications for wildlife management, where data are analyzed and decisions are made on a species-by-species basis. The graphs below show how variable the false-positive and false-negative probabilities were for each species, with the whiskers representing 95% confidence intervals.

Errors by species

We can conclude from these graphs that each species has a different set of considerations regarding these two errors. For example, deer and turkeys both have low false-negative and false-positive error rates, meaning that classifiers are good at correctly identifying these species and few are missed. Elk photos do not exhibit the same trends.

When a classifier identifies an elk in a photo, it is almost always an elk, but there are a fair number of photos of elk that are classified as some other species. For blank photos, the errors go in the opposite direction: if a photo is classified as blank, there is a ~25% probability that there is an animal in the photo, but there are very few blank photos that are incorrectly classified as having an animal in them.

Assessing species classifications with these two types of errors in mind helps us understand what we need to consider when determining final classifications of the data and its use for wildlife decision support.

Model success

When tested, the model was successful in identifying 97% of misclassified images. Factors considered in the development of the model included: differences in camera placement between sites; the way in which Zooniverse users interacted with the images; and more.

In general, the higher the proportion of users that agreed on the identity of the animal in the image, the greater the likelihood it was correct. Even seasonality was useful in evaluating accuracy for some species – snowshoe hares were found to be easily confused with cottontail rabbits in the summertime, when they both sport brown pelage.

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Not only does the information derived from this study have major implications for Snapshot Wisconsin, the framework for determining and remediating data quality presented in this article can benefit a broad range of big-data projects.

Rare Species Sighting: American Marten

In late February this year, a Snapshot Wisconsin trail camera deployed in Vilas County captured an American marten (Martes americana). This is the first time an American marten has been captured on a Snapshot Wisconsin camera! The below American marten was identified by the trail camera host, Ashley, and the identification was then confirmed by several species experts in the Wisconsin DNR. While American marten can vary in color, they are best identified by their pale buff to orange throats, dark legs and tails, vertical black lines running above the inner corners of their eyes, and bushy tails that account for one-third of their total length.

marten_SSWI000000011754918A (002)

American marten captured on a Vilas County Snapshot Wisconsin trail camera.

Extirpated from Wisconsin in the 1940’s, these small members of the weasel family were later reintroduced to the state and placed on the Wisconsin Endangered Species List in 1972 due to loss of suitable habitat. Marten are restricted to the northern portion of the state where they reside in dense, mature forests with preference for areas that are a mix of coniferous and deciduous trees.

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Counties shaded in blue have documented occurrences for American marten in the Wisconsin Natural Heritage Inventory database. The map is provided as a general reference of where occurrences of American marten meet NHI data standards and is not meant as a comprehensive map of all observations. Source: WNDR.

Did you know that marten are excellent climbers? They use this skill not only to hunt down prey, but also to avoid potential danger. These solitary animals are very territorial, with territories spanning an average of two square miles for males and one square mile for females. Although the breeding season lasts from July to August, fertilized eggs do not fasten to the uterine wall until January or February. Females birth two to four kits in March or April, and raise their young in tree dens without any male assistance.

There is still much to be learned about American marten, as their nocturnal lifestyle and often shy demeanor make them a difficult species to study. Follow this link for more information about American marten in Wisconsin, and stay tuned to discover what rare species will be captured next on Snapshot Wisconsin trail cameras!

April Science Update: Time-lapse Photos

The Snapshot Wisconsin team is often asked why we accept data only from our Snapshot-specific cameras. While there are several reasons, the reason that was highlighted in the April 2019 newsletter was because Snapshot Wisconsin cameras are programmed to take a single photo at 10:40 a.m. each day.  Although 10:40 may seem like an arbitrary time, this corresponds to the approximate time that a NASA satellite flies over Wisconsin and collects aerial imagery. (More information on how NASA data and Snapshot data are complementary can be found in this blog post.)

Timelapse

An example of a motion-triggered photo and a time-lapse photo from the same site in Sawyer County, September 2017.

It may be difficult to recognize the value of a blank photo in wildlife research, but a year-long series of these photos allows us to examine something very important to wildlife: habitat condition. For each camera site, the time-lapse photos are loaded into the statistical software, “R,” where each pixel in the image is analyzed and an overall measure of greenness is summarized for the entire photo. That measure, called the Green Chromatic Coordinate, can be used to identify different “phenophases,” or significant stages in the yearly cycle of a location’s plants and animals. These stages can be delineated on a graph, called a phenoplot, where a fitted curve reveals the transition day-by-day. The 2018 phenoplot for one Snapshot Wisconsin camera site is seen below.

PhenoPlot

 

PhenoPhases

Time-lapse photos from the corresponding phenophases as identified by the phenoplot above.

In 2018, 45 camera sites had a complete set of 365 time-lapse photos, but we expect many more sites to be included in the 2019 analyses. The relatively small sample size for 2018 is due in part to many counties not being opened for applications until partway through the year, but also because time-lapse data are rendered unusable if the date and time are not set properly on the camera. This may happen when the operator accidentally sets the time on the 12-hour clock instead of the 24-hour clock, or if the hardware malfunctions and resets the date and time to manufacturer settings—this is why we ask our volunteers to verify the camera’s date and time settings before leaving the site each time they perform a camera check.

The information derived from these analyses will be integrated into wildlife models. For example, the objective of one ongoing DNR research project is to understand linkages between deer body condition and habitat, which includes what’s available to deer as forest cover and food resources, as well as weather-related factors, such as winter severity or timing of spring greenup. The project currently uses weather data collected across the state to estimate snow depth, temperature, and winter severity, and creates maps based off this information.

Snapshot’s time-lapse cameras offer a wealth of seasonal information regarding type of forest cover and food sources, as well as weather-related information. In the future, phenological data obtained from Snapshot cameras could be used to create “greenup maps” that provide estimates of where and when greenup is occurring, and potentially test that information as a means of better understanding how environmental factors affect deer health, such as whether an early spring greenup improved deer body condition the next fall.

March Science Update: Greater-Prairie Chicken Lek Monitoring

One of Snapshot Wisconsin’s major goals is to alleviate some of the burden associated with time-consuming in-person survey techniques. This is possible because trail cameras can serve as round-the-clock observers in all weather conditions. Annual Greater Prairie-Chicken lekking (breeding) surveys were identified as having good potential to be supplemented by Snapshot Wisconsin cameras, and a pilot study was conducted in spring 2018.

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The Greater Prairie-Chicken (GPC) is a large grouse species native to grassland regions of central Wisconsin. During the breeding season each spring, males compete for female attention by creating a booming noise and displaying their specialized feathers and air sacks.  This ritual occurs on patches of land known as leks, as seen in the photo above. Wisconsin DNR Wildlife Management staff identify leks in the early spring and return to each site twice in the season to count the number of booming males. The number of males present on the leks is used as an index to population size. Three Snapshot Wisconsin cameras were deployed on each of five leks – one camera facing each direction except for east to reduce the number of photos triggered by the rising sun. The cameras were deployed from late March through mid-May, and all in-person surveying was conducted within the same period.

GPC_Method_Comparison

As seen in the graph above, Snapshot Wisconsin trail cameras recorded male GPC at all five of the study sites. This is significant because GPC were only detected on three of the five leks according to the in-person surveys. On leks A, B, and D, where both in-person and camera surveying detected GPC, the in-person maximum of male GPC was higher. However, when the trail camera maximum is averaged across all survey days, the maximum is nearly the same for both survey methods (8.5 in-person, 8.3 trail camera).

GPC_Hourly

In-person surveying requires the observers to arrive before dawn and remain in the blind until after the early morning booming has finished. Snapshot Wisconsin cameras record the hourly activity on the lek while minimizing the risk of disturbance due to human presence. The graph above displays the total number of male GPC photos captured by hour and shows a small uptick in photos around 7 p.m. Because the in-person surveys do not include evening observations, Snapshot Wisconsin data offer a way to examine the lek activity at all hours.

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Additionally, continuous data collection is not only useful in capturing the activity of GPC, but offers insight into the dynamics of Wisconsin’s grassland ecosystems. In total, Snapshot Wisconsin cameras collected over 3,000 animal images including badger, coyote, deer, other bird species, and more.  Some photos were even a little surprising.  Pictured above is a coyote just feet away from prairie chicken. We might expect the GPC to flee in the presence of a predator, but this one appears to be standing its ground. In the upcoming pilot year two, we hope to gather even more information about the interactions within and among species found on these leks.

February Science Update: Fawn to Doe Ratios

One of the major Wildlife Management implications for Snapshot Wisconsin is the project’s contributions toward a system the DNR uses to calculate the size of the white-tail deer population in Wisconsin. Fawn-to-doe ratios, or FDRs, are found by dividing the number of does by the number of fawns seen during the summer months and are summarized by the (82) management units across the state.

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In total, three programs contribute to FDR estimates: Snapshot Wisconsin, Operation Deer Watch, and the Summer Deer Observation Survey.  An advantage of incorporating Snapshot Wisconsin data in these estimates is that Snapshot cameras tend to be placed in secluded, natural areas, whereas the other two collection methods are opportunistic, meaning they’re biased toward counting deer seen near roadways.

One challenge associated with trail camera data is that the same individual animals may walk by the camera multiple times throughout the data collection period. To account for this, we average the total number of does seen in photos with at least one doe, and then average the total number of fawns in each photo containing at least one fawn.  We then take the average number of fawns and divide it by the average number of does.

Fawns and does may or may not be in the same photo to contribute to their respective averages. Defining a single camera-level average for each site drastically reduces the amount of data involved but ensures that the FDR is not skewed toward does, which tend to appear much more frequently on Snapshot cameras.

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2017 and 2018 Snapshot Wisconsin cameras contributing to FDR estimates. Thin grey lines delineate the deer management units, bold black lines define deer management zones.

The above maps show the camera sites that contributed to FDR estimates in 2017 and in 2018. Photos from exclusively July and August were analyzed. A site only contributes to the estimate if there were at least 10 doe observations in one of the two months, but can be counted twice if it had at least 10 doe observations in both months. Statewide, 897 cameras contributed to 2018 FDR estimates, a 44% increase from the 622 sites that contributed in 2017.  Some deer management units decreased in sample size from 2017, but

FDREstimates

2017 and 2018 Snapshot Wisconsin FDR estimates. Thin grey lines delineate the deer management units, bold black lines define deer management zones.

Above are the results of the 2017 and 2018 FDR estimates using Snapshot Wisconsin data. Only deer management units with a minimum of 5 camera sites were included in the analysis. In 2018, the range of FDR was 0.75 – 1.2, which is an overall increase from the range of 0.62 – 1.13 in 2017. Snapshot Wisconsin was launched statewide in August 2018, meaning most cameras in the newly open counties were not deployed until after the data collection period. We expect that the number of cameras in the 2019 analysis will increase again, which would give us even more accurate estimates.

January Science Update: Photo Category Breakdown

For January’s Science Update, also featured in The Snapshot monthly e-newsletter, we explored the accumulation of Snapshot Wisconsin photos over time and how the number of photos taken fluctuates with the seasons. To date, our data set contains more than 24 million photos, and their content is a vital component of the Snapshot Wisconsin project.

bargraphThe bar chart above indicates that over half of the photos are blank. This can be attributed to the fact that our cameras contain a motion trigger function, which is designed to capture wildlife as it moves through the frame. However, this mechanism only detects movement and cannot differentiate between animals and vegetation. This means that on windy days during the spring green up period, thousands of blank photos can be captured. Occasionally cameras will malfunction and continuously take blank photos without being triggered by motion. This issue was more prevalent with earlier versions of our cameras; the model we currently use does not take as many blank photos. Additionally, over time volunteers have learned that trimming vegetation in front of their camera helps prevent blank photos.

Every day at 10:40AM, the cameras are programmed to record a time lapse photo. This is not only to document the “spring green up” period and the “fall brown down” period, but also to sync ground-level measures of greenness with satellite data. These photos are primarily used by our partners at UW-Madison and compose 7% of our data set.

It is not uncommon for our trail camera hosts to trigger the camera themselves during check events, which is the cause of most of the 3% of photos that are tagged as human. Although these photos are removed from the data set prior to analysis, they can be helpful in instances where the camera has been recording photos with the wrong date and time. A photo of a hand in front of the camera combined with the date and time reported by the volunteer at each check event are enough for us to adjust the date and time for the whole set of photos.

Twenty percent of the Snapshot Wisconsin photos are untagged, meaning they have yet to be classified as blank, human or animal. Many of these photos will be sent to the crowd sourcing website, Zooniverse, for classification. We hope to implement a program to automatically classify photos to work through this backlog as well.

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Finally, about 14% of Snapshot Wisconsin photos are of confirmed animals. In the graph above, we have broken down which species appear in these photos. Deer are by far the most common species, appearing in about two-thirds of photos, followed by squirrels, raccoons, turkey, cottontail rabbits, coyotes, and elk. The remaining 8 percent of animal tags are divided up across 34 categories including other bird, opossum, snowshoe hare, bear, crane, and fox. Elk may have a higher proportion of triggers than expected because Snapshot Wisconsin cameras are placed more densely in the elk reintroduction areas than in other areas of the state.